siggraph05

Face Transfer is a method for mapping videorecorded performances
of one individual to facial animations of another. It extracts
visemes (speech-related mouth articulations), expressions,
and three-dimensional (3D) pose from monocular video or film
footage. These parameters are then used to generate and drive a
detailed 3D textured face mesh for a target identity, which can be
seamlessly rendered back into target footage. The underlying face
model automatically adjusts for how the target performs facial expressions
and visemes. The performance data can be easily edited
to change the visemes, expressions, pose, or even the identity of
the target—the attributes are separably controllable.

Style translation is the process of transforming an input motion into a new style while preserving its original content. This problem is motivated by the needs of interactive applications, which require rapid processing of captured performances. Our solution learns to translate by analyzing differences between performances of the same content in input and output styles. It relies on a novel correspondence algorithm to align motions, and a linear time-invariant model to represent stylistic differences. Once the model is estimated with system identification, our system is capable of translating streaming input with simple linear operations at each frame.

We present a signal-processing framework for light transport. We study the frequency content of radiance and how it is affected by phenomena such as shading, occlusion, and travel in free space. This extends previous work that considered either spatial or angular dimensions, and offers a comprehensive treatment of both space and angle. We characterize how the radiance signal is modified as light propagates and interacts with objects. In particular, we show that occlusion (a multiplication in the primal space) amounts in the Fourier domain to a convolution by the frequency content of the blocker. Propagation in free space corresponds to a shear in the space-angle frequency domain, while reflection on curved objects performs a different shear along the angular frequency axis.

We present motion magnification, a technique that acts like a microscope for visual motion. It can amplify subtle motions in a video sequence, allowing for visualization of deformations that would otherwise be invisible. To achieve motion magnification, we need to accurately measure visual motions, and group the pixels to be modified. After an initial image registration step, we measure motion by a robust analysis of feature point trajectories, and segment pixels based on similarity of position, color, and motion. A novel measure of motion similarity groups even very small motions according to correlation over time, which often relates to physical cause.

The ability to position a small subset of mesh vertices and produce a meaningful overall deformation of the entire mesh is a fundamental task in mesh editing and animation. However, the class of meaningful deformations varies from mesh to mesh and depends on mesh kinematics, which prescribes valid mesh configurations, and a selection mechanism for choosing among them. Drawing an analogy to the traditional use of skeleton-based inverse kinematics for posing skeletons, we define mesh-based inverse kinematics as the problem of finding meaningful mesh deformations that meet specified vertex constraints.
Our solution relies on example meshes to indicate the class of meaningful deformations.

Video matting is the process of pulling a high-quality alpha matte and foreground from a video sequence. Current techniques require either a known background (e.g., a blue screen) or extensive user interaction (e.g., to specify known foreground and background elements). The matting problem is generally under-constrained, since not enough information has been collected at capture time. We propose a novel, fully autonomous method for pulling a matte using multiple synchronized video streams that share a point of view but differ in their plane of focus. The solution is obtained by directly minimizing the error in filter-based image formation equations, which are over-constrained by our rich data stream.

Deformation transfer applies the deformation exhibited by a source triangle mesh onto a different target triangle mesh. Our approach is general and does not require the source and target to share the same number of vertices or triangles, or to have identical connectivity. The user builds a correspondence map between the triangles of the source and those of the target by specifying a small set of vertex markers. Deformation transfer computes the set of transformations induced by the deformation of the source mesh, maps the transformations through the correspondence from the source to the target, and solves an optimization problem to consistently apply the transformations to the target shape.